AIMC Topic: Drug Interactions
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MGDDI: A multi-scale graph neural networks for drug-drug interaction prediction.
Methods (San Diego, Calif.)
May 15, 2024
Drug-drug interaction (DDI) prediction is crucial for identifying interactions within drug combinations, especially adverse effects due to physicochemical incompatibility. While current methods have made strides in predicting adverse drug interaction...
DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy.
NPJ systems biology and applications
May 6, 2024
Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Com...
Learning with an evolving medicine label: how artificial intelligence-based medication recommendation systems must adapt to changing medication labels.
Expert opinion on drug safety
May 6, 2024
INTRODUCTION: Artificial intelligence or machine learning (AI/ML) based systems can help personalize prescribing decisions for individual patients. The recommendations of these clinical decision support systems must relate to the "label" of the medic...
Timing Matters: A Machine Learning Method for the Prioritization of Drug-Drug Interactions Through Signal Detection in the FDA Adverse Event Reporting System and Their Relationship with Time of Co-exposure.
Drug safety
Apr 30, 2024
INTRODUCTION: Current drug-drug interaction (DDI) detection methods often miss the aspect of temporal plausibility, leading to false-positive disproportionality signals in spontaneous reporting system (SRS) databases.
Drug-Online: an online platform for drug-target interaction, affinity, and binding sites identification using deep learning.
BMC bioinformatics
Apr 20, 2024
BACKGROUND: Accurately identifying drug-target interaction (DTI), affinity (DTA), and binding sites (DTS) is crucial for drug screening, repositioning, and design, as well as for understanding the functions of target. Although there are a few online ...
SubGE-DDI: A new prediction model for drug-drug interaction established through biomedical texts and drug-pairs knowledge subgraph enhancement.
PLoS computational biology
Apr 16, 2024
Biomedical texts provide important data for investigating drug-drug interactions (DDIs) in the field of pharmacovigilance. Although researchers have attempted to investigate DDIs from biomedical texts and predict unknown DDIs, the lack of accurate ma...
Harnessing machine learning to predict cytochrome P450 inhibition through molecular properties.
Archives of toxicology
Apr 15, 2024
Cytochrome P450 enzymes are a superfamily of enzymes responsible for the metabolism of a variety of medicines and xenobiotics. Among the Cytochrome P450 family, five isozymes that include 1A2, 2C9, 2C19, 2D6, and 3A4 are most important for the metabo...
Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition.
Chemical research in toxicology
Mar 19, 2024
Most drugs are mainly metabolized by cytochrome P450 (CYP450), which can lead to drug-drug interactions (DDI). Specifically, time-dependent inhibition (TDI) of CYP3A4 isoenzyme has been associated with clinically relevant DDI. To overcome potential D...
Antibiotic combinations prediction based on machine learning to multicentre clinical data and drug interaction correlation.
International journal of antimicrobial agents
Feb 29, 2024
BACKGROUND: With increasing antibiotic resistance and regulation, the issue of antibiotic combination has been emphasised. However, antibiotic combination prescribing lacks a rapid identification of feasibility, while its risk of drug interactions is...
HormoNet: a deep learning approach for hormone-drug interaction prediction.
BMC bioinformatics
Feb 28, 2024
Several experimental evidences have shown that the human endogenous hormones can interact with drugs in many ways and affect drug efficacy. The hormone drug interactions (HDI) are essential for drug treatment and precision medicine; therefore, it is ...
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